{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 4: Analysis and Post-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook covers the basics of data analysis and post-processing using Dedalus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll import the public interface and suppress some of the logging messages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dedalus import public as de\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "de.logging_setup.rootlogger.setLevel('ERROR')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1: Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dedalus includes a framework for evaluating and saving arbitrary analysis tasks while an initial value problem is running.  To get started, let's setup the KdV-Burgers problem from the previous tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Field 140247894405976>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create basis and domain\n",
    "x_basis = de.Chebyshev('x', 1024, interval=(-2, 6), dealias=3/2)\n",
    "domain = de.Domain([x_basis], np.float64)\n",
    "\n",
    "# Create problem\n",
    "problem = de.IVP(domain, variables=['u', 'ux', 'uxx'])\n",
    "problem.meta[:]['x']['dirichlet'] = True\n",
    "problem.parameters['a'] = 2e-4\n",
    "problem.parameters['b'] = 1e-4\n",
    "problem.add_equation(\"dt(u) - a*dx(ux) - b*dx(uxx) = -u*ux\")\n",
    "problem.add_equation(\"ux - dx(u) = 0\")\n",
    "problem.add_equation(\"uxx - dx(ux) = 0\")\n",
    "problem.add_bc('left(u) = 0')\n",
    "problem.add_bc('left(ux) = 0')\n",
    "problem.add_bc('right(ux) = 0')\n",
    "\n",
    "# Build solver\n",
    "solver = problem.build_solver(de.timesteppers.RK443)\n",
    "solver.stop_sim_time = np.inf\n",
    "solver.stop_wall_time = np.inf\n",
    "solver.stop_iteration = 1000\n",
    "\n",
    "# Reference local grid and state fields\n",
    "x = domain.grid(0)\n",
    "u = solver.state['u']\n",
    "ux = solver.state['ux']\n",
    "uxx = solver.state['uxx']\n",
    "\n",
    "# Setup smooth triangle with support in (-1, 1)\n",
    "n = 20\n",
    "u['g'] = np.log(1 + np.cosh(n)**2/np.cosh(n*x)**2) / (2*n)\n",
    "u.differentiate('x', out=ux)\n",
    "ux.differentiate('x', out=uxx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis handlers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The explicit evaluation of analysis tasks during timestepping is controlled by the `solver.evaluator` object.  Various handler objects are attached to the evaluator, and control when the evaluator computes their own set of tasks and what happens to the resulting data.\n",
    "\n",
    "For example, an internal `SystemHandler` object directs the evaluator to evaluate the RHS expressions on every iteration, and uses the data for the explicit part of the timestepping algorithm.\n",
    "\n",
    "For simulation analysis, the most useful handler is the `FileHandler`, which regularly computes tasks and writes the data to HDF5 files.  When setting up a file handler, you specify the name/path for the output directory/files, as well as the cadence at which you want the handler's tasks to be evaluated.  This cadence can be in terms of any combination of \n",
    "* simulation time, specified with `sim_dt`\n",
    "* wall time, specified with `wall_dt`\n",
    "* iteration number, specified with `iter`\n",
    "\n",
    "To limit file sizes, the output from a file handler is split up into different sets over time, each containing some number of writes that can be limited with the `max_writes` keyword when the file handler is constructed.\n",
    "\n",
    "Let's setup a file handler to be evaluated every few iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis = solver.evaluator.add_file_handler('analysis', iter=5, max_writes=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add an arbitrary number of file handlers to save different sets of tasks at different cadences and to different files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis tasks are added to a given handler using the `add_task` method.  Tasks are entered in plain text, and parsed using the same namespace that is used for equation entry.  For each task, you can additionally specify the output layout and scaling factors.  \n",
    "\n",
    "Let's add tasks for tracking the first and second moments of the solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis.add_task(\"integ(u,'x')\", layout='g', name='<u>')\n",
    "analysis.add_task(\"integ(u**2,'x')\", layout='g', name='<uu>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For checkpointing, you can also simply specify that all of the state variables should be saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis.add_system(solver.state, layout='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now run the simulation just as in the previous tutorial, but without needing to manually save any data during the main loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed iteration 100\n",
      "Completed iteration 200\n",
      "Completed iteration 300\n",
      "Completed iteration 400\n",
      "Completed iteration 500\n",
      "Completed iteration 600\n",
      "Completed iteration 700\n",
      "Completed iteration 800\n",
      "Completed iteration 900\n",
      "Completed iteration 1000\n",
      "Runtime: 8.055663108825684\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "# Main loop\n",
    "dt = 1e-2\n",
    "start_time = time.time()\n",
    "while solver.ok:\n",
    "    solver.step(dt)\n",
    "    if solver.iteration % 100 == 0:\n",
    "        print('Completed iteration {}'.format(solver.iteration))\n",
    "end_time = time.time()\n",
    "print('Runtime:', end_time-start_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2: Post-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File arrangement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the output files for each file handler are arranged as follows:\n",
    "1. A base folder taking the name that was specified when the file handler was constructed, e.g. `analysis/`.\n",
    "2. Within the base folder are subfolders for each set of outputs, with the same name plus a set number, e.g. `analysis_s0/`.\n",
    "3. Within each set subfolder are HDF5 files for each process, with the same name plus a process number, e.g. `analysis_s0_p1.h5`.\n",
    "\n",
    "Let's take a look at the output files from our example problem.  We should see two sets (1000 total iterations, 5 iteration cadence, 100 writes per file) and data from one process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analysis\n",
      "analysis/analysis_s1\n",
      "analysis/analysis_s1/analysis_s1_p0.h5\n",
      "analysis/analysis_s2\n",
      "analysis/analysis_s2/analysis_s2_p0.h5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "print(subprocess.check_output(\"find analysis\", shell=True).decode())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging output files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, each process writes its local portion of the analysis tasks to its own file, but often it is substantially easier to deal with the global dataset.  The distributed process files can be easily merged into a global file for each set using the `merge_process_files` function from the `dedalus.tools.post` module.\n",
    "\n",
    "Since we ran this problem serially, here this will essentially just perform a copy of the root process file, but we'll do the merge for illustrative purposes, anyways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dedalus.tools import post\n",
    "post.merge_process_files(\"analysis\", cleanup=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the merge, we see that instead of a subfolder and process files for each output set, we have a single global set file for each output set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analysis\n",
      "analysis/analysis_s1.h5\n",
      "analysis/analysis_s2.h5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "print(subprocess.check_output(\"find analysis\", shell=True).decode())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For some types of analysis, it's additionally convenient to merge the output sets together into a single file that's global in space and time, which can be done with the `merge_sets` function.  \n",
    "\n",
    "**Note**: this can generate very large files, so it's not recommended for analysis that is simply slicing over time, e.g. plotting snapshots of an analysis task at different times.  However, if you want to explicitly plot a quantity versus time, instead of slicing over time, it can be useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "set_paths = list(pathlib.Path(\"analysis\").glob(\"analysis_s*.h5\"))\n",
    "post.merge_sets(\"analysis/analysis.h5\", set_paths, cleanup=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we see that the two sets have been merged into a single file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analysis\n",
      "analysis/analysis.h5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "print(subprocess.check_output(\"find analysis\", shell=True).decode())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each HDF5 file contains a \"tasks\" group containing a dataset for each task assigned to the file handler.  The first dimension of the dataset is time,  and the subsequent dimensions are the spatial dimensions of the output field.\n",
    "\n",
    "The HDF5 datasets are self-describing, with dimensional scales attached to each axis.  For the first axis, these include the simulation time, wall time, iteration, and write number.  The scales indicate grid points or mode numbers for the spatial axes, based on the task layout.  See the [h5py docs](http://docs.h5py.org/en/latest/) for more details.\n",
    "\n",
    "Let's open up the merged analysis file and plot time series of the moments.  We expect the first moment (momentum) to be conserved through the simulation, while the second moment (kinetic energy) should decay due to dissipation in the shock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h5py\n",
    "\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "with h5py.File(\"analysis/analysis.h5\", mode='r') as file:\n",
    "    u1 = file['tasks']['<u>']\n",
    "    u2 = file['tasks']['<uu>']\n",
    "    t = file['scales']['sim_time']\n",
    "    plt.plot(t, u1, label=\"<u>\")\n",
    "    plt.plot(t, u2, label=\"<uu>\")\n",
    "    plt.ylim(0, 1.5)\n",
    "    plt.legend(loc='upper right', fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also pass datasets, like fields, to the plotting helper functions in the `plot_tools` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kburns/Dropbox/Git/dedalus/dedalus/extras/plot_tools.py:116: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. In future versions, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = copy.copy(mpl.cm.get_cmap(\"RdBu_r\"))\n",
      "  cmap.set_bad('0.7')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dedalus.extras import plot_tools\n",
    "\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "with h5py.File(\"analysis/analysis.h5\", mode='r') as file:\n",
    "    u = file['tasks']['u']\n",
    "    plot_tools.plot_bot_2d(u, title=\"A dispersive shock!\", transpose=True, axes=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's cleanup the analysis files we created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil\n",
    "shutil.rmtree('analysis')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:dedalus]",
   "language": "python",
   "name": "conda-env-dedalus-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
